Linear Programming and AI Optimization Models VOL-3
Foundations Algorithms and Modern Applications in Operations Research and Machine Learning
Linear Programming and AI Optimization Models (VOL-3) delivers practical mastery in scheduling, supply chain, logistics, hybrid AI-OR systems, large-scale cloud optimization, and emerging technologies. With Python implementations, industry case studies, and forward-looking research trends, this volume turns theory into powerful real-world solutions.
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About
About the Book
Linear Programming and AI Optimization Models: Foundations, Algorithms, and Modern Applications in Operations Research and Machine Learning (VOL-3) completes the comprehensive three-volume series by focusing on practical, large-scale, and emerging applications of optimization in real-world systems.
This final volume emphasizes applied optimization in scheduling, supply chain and logistics, hybrid AI-OR models, large-scale distributed systems, and cutting-edge research trends. It bridges theory with practice through extensive Python implementations, rich case studies across industries, and forward-looking discussions on quantum optimization, LLM-assisted methods, and sustainable AI systems.
Key Features:
- In-depth coverage of scheduling, resource allocation, vehicle routing, and supply chain optimization
- Modern frameworks including Bayesian Optimization, Hybrid OR-ML models, and Neuro-Symbolic approaches
- Practical implementation chapter with Python tools (PuLP, OR-Tools, CP-SAT, PyTorch, etc.)
- Real-world case studies from airline, healthcare, e-commerce, manufacturing, and robotics
- Exploration of emerging trends: Quantum Optimization, Differentiable Programming, Multi-Agent Systems, and Sustainability
This volume is ideal for practitioners, researchers, and advanced students who want to apply powerful optimization techniques to solve complex industrial and AI problems while staying ahead of future developments.
Together with Volume-1 (Foundations) and Volume-2 (AI-Driven Models), this series provides one of the most complete resources available on optimization for Operations Research and Machine Learning.
Author
About the Author
Anshuman Kumar Mishra, M.Tech (Computer Science) Assistant Professor, Doranda College, Ranchi University
Prolific Author of 50+ Books on AI, Machine Learning & Computer Science | 20+ Years Experience
Anshuman Kumar Mishra is a dedicated educator, researcher, and highly prolific author with over 20 years of experience in Computer Science and Information Technology. Holding an M.Tech in Computer Science from BIT Mesra, he brings a rare combination of academic depth and practical teaching expertise.
Currently serving as Assistant Professor at Doranda College under Ranchi University, he has mentored thousands of students, helping them build strong foundations in programming, data science, and artificial intelligence. His student-centric teaching style emphasizes conceptual clarity, hands-on practice, and real-world application.
Anshuman is a prolific author with more than 50 books published across a wide spectrum of computer science and emerging technology domains. From foundational programming languages to advanced topics in Artificial Intelligence, Machine Learning, Reinforcement Learning, Decision Theory, and Computer Vision — his books are widely appreciated by students, educators, and professionals for their clear explanations, strong theoretical foundation, and practical approach.
His extensive body of work reflects his deep commitment to making complex subjects accessible and meaningful for learners at all levels. He is particularly recognized for creating well-structured learning paths that help readers progress from beginner to advanced levels with confidence.
Driven by the mission to democratize quality technical education, Anshuman continues to write and update books that bridge the gap between academic theory and industry practice.
When not teaching or writing, he actively follows and explores new developments in AI, Quantum Machine Learning, and Ethical Intelligence systems.
Contents
Table of Contents
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